Fix model.get (#1287)

* fix

* fix

* fix

* fix test

* add docstring

---------

Co-authored-by: Eugene Khvedchenya <ekhvedchenya@gmail.com>

src/super_gradients/training/models/model_factory.py

@@ -103,7 +103,10 @@ def instantiate_model(
     :param arch_params:         Architecture hyper parameters. e.g.: block, num_blocks, etc.
     :param num_classes:         Number of classes (defines the net's structure).
                                     If None is given, will try to derrive from pretrained_weight's corresponding dataset.
-    :param pretrained_weights:  Describe the dataset of the pretrained weights (for example "imagenent")
+    :param pretrained_weights:  Describe the dataset of the pretrained weights (for example "imagenent").
+                                Add `platform/` prefix if the weights are stored in the platform -
+                                Please note that in this case, `num_classes` is expected to be the checkpoints number of classes, and not the number of class
+                                that you want to use - you will need to replace the head afterward if you want to work with a different number of classes.
     :param download_required_code: if model is not found in SG and is downloaded from a remote client, overriding this parameter with False
                                 will prevent additional code from being downloaded. This affects only models from remote client.
 
@@ -132,11 +135,15 @@ def instantiate_model(
         if pretrained_weights is None and num_classes is None:
             raise ValueError("num_classes or pretrained_weights must be passed to determine net's structure.")
 
-        if pretrained_weights and pretrained_weights in PRETRAINED_NUM_CLASSES.keys():
-            num_classes_new_head = core_utils.get_param(arch_params, "num_classes", PRETRAINED_NUM_CLASSES[pretrained_weights])
-            arch_params.num_classes = PRETRAINED_NUM_CLASSES[pretrained_weights]
-        elif pretrained_weights and pretrained_weights is None:
-            raise ValueError(f"Unknown pretrained_weights - couldn't find pretrained weights in {PRETRAINED_NUM_CLASSES.keys()} or platform.")
+        if pretrained_weights:
+            if pretrained_weights in PRETRAINED_NUM_CLASSES.keys():
+                num_classes_new_head = core_utils.get_param(arch_params, "num_classes", PRETRAINED_NUM_CLASSES[pretrained_weights])
+                arch_params.num_classes = PRETRAINED_NUM_CLASSES[pretrained_weights]
+            elif not download_platform_weights:
+                raise ValueError(
+                    f'`pretrained_weights="{pretrained_weights}"` is not a valid and was not found in that platform. '
+                    f'Valid pretrained weights are: "{PRETRAINED_NUM_CLASSES.keys()}"'
+                )
 
         # Most of the SG models work with a single params names "arch_params" of type HpmStruct, but a few take
         # **kwargs instead
@@ -151,7 +158,7 @@ def instantiate_model(
             else:
                 load_pretrained_weights(net, model_name, pretrained_weights)
 
-            if num_classes_new_head != arch_params.num_classes:
+            if pretrained_weights in PRETRAINED_NUM_CLASSES.keys() and num_classes_new_head != arch_params.num_classes:
                 net.replace_head(new_num_classes=num_classes_new_head)
                 arch_params.num_classes = num_classes_new_head